WXS of 147 lung cancer patients treated with immunotherapy
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ABSTRACT:
PROVIDER: EGAS00001003781 | EGA |
REPOSITORIES: EGA
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2014-11-01 | E-GEOD-51875 | biostudies-arrayexpress
Project description:Inferring phenotypic outcome from genomics features is the promise and challenge for basic and applied research. Functionally testing whether the feature(s) with predictive power can shed light on the underlying mechanism remains largely unexplored. We address these gaps by applying a machine learning approach to predict phenotypes based on transcriptome data. We validate the predictive models in silico using out-of-sample varieties and in planta by reverse genetics. Our study primarily focuses on a agronomic trait nitrogen use efficiency (NUE), but the described approach can be applied to any phenotype across biology, agriculture and medicine. We measure NUE and gene expression from natural and cultivated varieties of Arabidopsis and maize, respectively, under low and high N environments. We integrate transcriptomics across a model and a crop and demonstrate that identifying the evolutionarily conserved N-responsive differentially expressed genes is an efficient technique to reduce the feature dimensionality, which ultimately improves the predictive power of the gene-to-trait models learned. Furthermore, including a model organism with a comprehensive mutant collection has enabled the efficient validation of the role candidate genes with predictive power in NUE outcomes. Using the matched transcriptomic and phenotypic data from a model and crop species, we present an analysis pipeline to infer a complex trait from transcript abundance. This analysis pipeline can be applied to any other organisms to uncover novel genes underpinning physiological traits of interest.
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2016-06-09 | GSE83131 | GEO
Project description:Oxaliplatin (oxPt) resistance in colorectal cancers (CRC) is a major medical problem, and predictive markers are urgently needed. Recently, miR-625-3p was reported as a promising predictive marker. Here, we have used in vitro models to show that miR-625-3p functionally induces oxPt resistance in CRC cells, and have identified signalling networks affected by miR-625-3p. The p38 MAPK activator MAP2K6 was shown to be a direct target of miR-625-3p, and, accordingly, was downregulated in patients not responding to oxPt therapy. miR-625-3p resistance could be reversed in CRC cells by anti-miR-625-3p treatment and by ectopic expression of a miR-625-3p insensitive MAP2K6 variant. In addition, by reducing p38 MAPK signalling using either siRNA technology, chemical inhibitors to p38 or by ectopic expression of dominant negative MAP2K6 protein we induced resistance to oxPt. Transcriptome, proteome and phosphoproteome profiles revealed inactivation of MAP2K6-p38 signalling as one likely mechanism a possible driving force behind of oxPt resistance. Our study shows that miR-625-3p induces oxPt resistance by abrogating MAP2K6-p38 regulated apoptosis and cell cycle control networks, and corroborates the predictive power of miR-625-3p
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Project description:A major goal of biology is the construction of networks that predict complex system behavior. We combine multiple types of molecular data, including genotypic, expression, transcription factor binding site (TFBS), and protein-protein interaction (PPI) data previously generated from a number of yeast experiments in order to reconstruct causal gene networks. Networks based on different types of data are compared using metrics devised to assess the predictive power of a network. A network reconstructed by integrating genotypic, TFBS and PPI data is shown to be the most predictive. This network is used to predict causal regulators responsible for hot spots of gene expression activity in a segregating yeast population. The network is also shown to elucidate the mechanisms by which causal regulators give rise to larger-scale changes in gene expression activity. Predictions are prospectively validated to provide direct experimental evidence that predictive networks can be constructed by integrating multiple, appropriate data types. Keywords: allele replacement Allele replacement strains were created in two backgrounds: BY (BY4724 and BY4742), and RM11-1a, and compared to the parental type (BY4716 and RM11-1a). BY7g, BY9g, BY11g are BY4716, while RM7g, RM9g, and RM11g are RM11-1a. YAD350 is a MKT1 D30G replacement strain in BY4742 background. YLK804 is a SAL1-RM allele in BY4724 and YLK806 is a SAL1-BY allele in RM11-1a. All strains were grown in YNB + 2% glucose, with leucine, lysine, and uracil. YAD350 was also grown with histidine.
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